3D Point Data Alignment via Nearest Neighbor Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The manufacturing process of components, such as aircraft components, faces challenges in accurately aligning multiple 3D scans of large objects, which is time-consuming and costly when done manually, and computationally prohibitive when attempting automatic alignment of large overlapping scans.
Innovation Solution
An apparatus and method that utilize a nearest neighbor search engine to identify closest corresponding points in overlapping sets of 3D surface data, generating transformation matrices to align these points, and iteratively apply these matrices to reduce a global distance metric, enabling automated and accurate alignment of 3D point data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alignment of scan data is performed, then alignment accuracy can be achieved, but time consumption and cost increase significantly
Solution Approach 1:
The system performs automatic alignment by having the scan data itself provide the alignment information through feature detection and matching algorithms. The computer automatically processes the scan data to identify corresponding features and compute transformation matrices, eliminating the need for manual intervention while maintaining alignment accuracy.
Solution Approach 2:
The patent replaces manual mechanical alignment operations with automated computational processes. Instead of manual measurement and adjustment, the system uses computer algorithms to detect features, match points, and calculate transformation matrices, substituting human-operated mechanical processes with automated information processing.
2Productivity
If automatic alignment of large number of overlapping scans is performed, then time consumption is reduced, but computational complexity becomes prohibitive
Solution Approach 1:
The patent segments the alignment process into distinct stages: feature detection, feature matching, and transformation matrix computation. It also segments the scan data processing by identifying overlapping regions and processing them in pairs, reducing the overall computational burden compared to processing all scans simultaneously.
Solution Approach 2:
The system extracts only the essential alignment information from the scan data by detecting salient features and matching corresponding points. Instead of processing entire point clouds, it extracts and processes only the feature points necessary for alignment, significantly reducing computational complexity while maintaining alignment accuracy.
3Reliability
If traditional alignment methods are used for multiple 3D scans, then comprehensive coverage is achieved, but processing complexity increases exponentially
Solution Approach 1:
The patent segments the multiple scan alignment problem into pairwise comparisons of overlapping scans. By processing scans in pairs that share overlapping regions and using transitive relationships, it achieves comprehensive alignment without the exponential complexity of comparing all scans against each other.
Solution Approach 2:
The system uses overlapping scan regions as intermediaries to establish relationships between multiple scans. The overlapping portions serve as reference frames that connect different scan sets, enabling transitive alignment through a series of pairwise transformations rather than direct multi-set comparison.
Data Source
AI summary
An apparatus includes a nearest neighbor search engine configured to receive multiple sets of surface data. Each of the multiple sets includes three-dimensional point positions of a corresponding portion of a surface of an object. The nearest neighbor search engine is configured to perform a nearest neighbor search based on adjacency data indicating pairs of overlapping sets of the multiple sets to identify, in each pair of overlapping sets, pairs of closest corresponding points that are within a threshold distance of each other. The apparatus includes a transformation matrix generator configured to determine one or more transformation matrices to reduce a global distance metric that corresponds to distances between the points of each identified pair of closest corresponding points. The apparatus also includes a data set aligner configured to generate updated 3D point positions by applying the one or more transformation matrices to one or more of the multiple sets.